High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 9; p. 1647
• Main Authors: Xue, Min, Xing, Mengdao, Gao, Yuexin, Fu, Jixiang, Wu, Zhixin, Tang, Wangshuo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accuracy; adaptive hybrid re-weighted constraint (AHRC); Algorithms; Bandwidths; Channels; Clutter; Cross correlation; Deep learning; Errors; Fourier transforms; High resolution; high-resolution ranging; Lasers; Lidar; low SNR; Mathematical models; Methods; Optimization; Radar; Registration; restricted isometry property-based optimal multi-channel coprime sampling (RIP-OMCS); Sampling; Signal to noise ratio; Spectrum allocation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16091647

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method.